Light and Fast Statistical Motion Detection Method Based on Ergodic Model

In this paper, we propose a light and fast pixel-based statistical motion detection method based on a background subtraction procedure. The statistical representation of the background relies on its spatial color distributions herein modeled by a mixture of Gaussians. The Gaussian parameters are obtained after segmenting one reference frame with an unsupervised Bayesian approach whose parameter estimation step is ensured by the ? -Means and the Iterated Conditional Estimation (ICE) algorithms. Since the motion detection function only depends on a global mixture of ? Gaussians, only a few bits per pixel need to be stored in memory. Our method achieves real-time performances, especially when look up tables are used to store pre-calculated data. Results have been obtained on synthetic and real video sequences and compared with other statistical methods.

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Pierre-Marc Jodoin, Max Mignotte, Janusz Konrad
• Keywords: Background Subtraction Procedure, ICIP 2006, Image Processing, Iterated Conditional Estimation, Motion Detection Function, Parameter Estimation Step, Statistical Motion Detection